Preface of ISAI2020

The International Conference on Information Systems and Artificial Intelligence is an academic conference on the advancement of artificial intelligence and information systems. The conference includes the fields of machine vision, natural language processing, speech recognition and the industry application of AI, and is of great significance to the development of intelligent machines, intelligent human-computer interaction, and effective use of information, human health and social security.The primary goal of the conference is to promote research and developmental activities in information system and artificial intelligence and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in information system and artificial intelligence and related areas. In order to further promote the development of artificial intelligence and information systems disciplines and strengthen domestic and international academic exchanges and cooperation.

Preface: ICACM-2018It is our great pleasure to have you to the International Conference on Applied and Computational Mathematics (ICACM-2018) (in honour of Dr. S. Rajan) and was hosted by Erode Arts and Science College, Erode, Tamil Nadu, India, which was held in 10th August 2018. Research Scholars (Ph.D. & M. Phil.) of Dr. S. Rajan and Mudaliar Educational Trust, Erode financially supported the conference and attended by many academicians, researchers and postgraduate students.The main objective of this conference is to provide a platform to deliberate latest developments and future directions in Applied and Computational Mathematics. Resources persons from different parts of India and other countries have delivered expert is electures by imparting the latest findings from their respective research domains. It was a wonderful opportunity for the students, researchers and engineers to interact with the experts and specialists to get their advice or suggestions.We have received tremendous response (118 full-length paper submissions) from all over India and abroad (Malaysia, Saudi Arabia, Spain). Through a rigorous peer-review process, all submissions were performed double blind review to check their quality of content, level of innovation, significance, originality and legibility. Based on the expertise review comments, only 89 papers were accepted and the corresponding authors were invited to submit their papers for final Publication. The accepted papers are published by IOP Conference Series “Journal of Physics: Conference Series (JPCS)”.We would like to express our gratitude to the organizers, invited speakers and all the participants of the conference for their fruitful work and their excellent contribution to the success of the conference. The editors are extremely thankful to all the reviewers for providing constructive criticisms, stimulated comments and suggestions to the authors. Also, our heartfelt thanks and appreciation to the Editors and Managers, IOP Publishing Ltd. for their help and kind cooperation during the preparation of the proceedings.We hope you will benefit from all the manuscripts of the Proceedings and learn new and novel ideas.Thank you.The Editors,Dr. S. SivasankaranDr. S. Eswaramoorthi

The history of artificial intelligence (AI), from its conception to the creation of general AI (Gen-AI), is a fascinating story of human inventiveness, technical growth, and philosophical research. This article examines the historical milestones, major inventions, and transformational concepts that have influenced AI's trajectory. Beginning with early symbolic AI and rule-based systems, it investigates the shift to machine learning, highlighting discoveries in neural networks and deep learning that transformed disciplines such as computer vision and natural language processing. The introduction of generative models, such as GANs and VAEs, resulted in a considerable increase in AI capabilities, paving the path for Gen-AI. Unlike narrow AI, Gen-AI strives to imitate human-like intelligence and adaptability across a wide range of jobs, bringing serious ethical and philosophical concerns. This essay also looks at the current state of Gen-AI, its problems, and possible applications in healthcare, education, finance, and other areas. It finishes by picturing a future in which human-machine collaboration and ethical AI development are prioritized, highlighting the need of continual learning and responsible innovation.

Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogically structured frameworks that address non‐technical dimensions such as ethics, contextual understanding and the development of human‐centered, critical and lifelong learning mindsets to adapt to rapid technology changes. Furthermore, limitation of time in teacher education syllabus and faculty's lack of AI literacy require targeted intervention. This design‐based research (DBR) aims to fill this gap by developing and evaluating a set of design principles for GenAI literacy training in teacher education. Integrating contemporary AI competency frameworks for learners and teachers, the study implements these principles in a workshop that serves as the research prototype. The prototype was first piloted with 14 master's students and then evaluated with 29 teacher education (TE) students. Results show significant gains in participants' AI competence self‐efficacy, attitudes towards GenAI and commitment to critical, ethical and pedagogical engagement with GenAI tools. The findings highlight the need for teacher education programmes to integrate GenAI literacy that supports teachers' evolving roles as reflective practitioners, co‐creators and lifelong learners in an AI‐driven world. Practitioner notes What is already known about this topic Students' self‐efficacy and mindset influence their adoption of GenAI in learning and teaching. GenAI competency is essential for teacher education students, encompassing technological, ethical and sociocultural dimensions. Implementing AI literacy training faces challenges such as educators' limited AI literacy, the lack of pedagogical frameworks and the need to integrate technical knowledge with ethical reasoning, self‐regulated learning and metacognitive skills. Transformative learning is crucial in preparing teacher education students as co‐creators and facilitators in AI‐enhanced education. What this paper adds This study presents the design and evaluation of an AI competency training programme to enhance teacher education students' self‐efficacy, critical and human‐centered AI mindsets and human–AI interaction skills. Using design‐based research, it identifies key principles for AI literacy training, demonstrating how transformative, active and experiential learning approaches can prepare teacher education students for evolving roles in AI‐enhanced education. Implications for practice and/or policy Educational technologists can apply the proposed design principles to develop GenAI tools and AI systems that enhance AI competency and self‐regulated learning (SRL). Educators can integrate these AI training principles into their teaching strategies and enhance their task assignments and assessments to foster deep learning, self‐regulated learning and metacognitive skills in the age of GenAI. Policymakers should update teacher education curriculum to include AI competency, strengthen content and pedagogical knowledge, especially in social emotional learning, formative and authentic assessment methods and establish guidelines for selecting GenAI tools that support human intelligence and mitigate over‐reliance.

ChatGPT, an interactive, generative artificial intelligence (AI) system, was introduced in late 2022, quickly becoming one of the most rapidly adopted technologies in history. The rapid emergence of ChatGPT and similar AI tools, such as Google’s Bard, and GPT-enabled Bing from Microsoft have led to intense discussions about how they will affect various aspects of society, including higher education. Information systems (IS) education will not escape the impact of AI tools. Our goal for this paper is to develop a better understanding of the range of possible impacts of ChatGPT on IS education and to describe how IS educators might respond to these potential impacts. To that end, we discuss challenges for IS education brought on by generative AI tools, and discuss potential future scenarios based on the emergence of such tools, ranging from AI having little impact on IS education to AI serving as competition for IS educators. We examine the challenges and consequences of each scenario. We also discuss potential responses, ranging from doing nothing to embracing AI tools as legitimate learning aids. We then provide several specific recommendations that will allow IS educators to effectively respond to the rise of AI tools.

he rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.

As organizations increasingly navigate the complexities of the digital age, the incorporation of artificial intelligence (AI) into knowledge management (KM) frameworks is becoming indispensable for the attainment of sustained competitive advantage. This chapter explores the transformative capabilities of AI within KM, underscoring the pivotal significance of human–AI collaboration. We will investigate how emerging technologies, including AI, machine learning (ML) and natural language processing (NLP), can enhance key KM dimensions: the creation, storage, retrieval, sharing and application of knowledge. Furthermore, the chapter considers the essential contributions of human expertise, contextual understanding and decision-making in KM practices. By elucidating the synergy between the advantages of AI, like scalability, data analysis, human competencies, creativity and empathy, the chapter further explores specific implementations of AI–human collaboration, encompassing automated knowledge repositories for knowledge generation, NLP for knowledge retrieval and tailored recommendations for knowledge application. We will also address the ethical challenges and implications that accompany AI integration, focusing on issues such as bias, the need for transparency, trust in AI systems, data privacy and the balance between human oversight and AI control. Finally, we outline practical implications for organizations aiming to empower talent, create AI-friendly infrastructures and streamline knowledge processes to foster successful human–AI partnerships. We conclude by discussing the future of AI–human collaboration in KM, identifying emerging trends and the long-term benefits of integrating AI into organizational learning and innovation.

Continual learning in medical devices: FDA's action plan and beyond

Evaluating generative AI responses to real-world drug-related questions

Aim/Purpose In a post-pandemic learning environment, artificial intelligence (AI) may become the new standard. More and more college students are utilizing AI tools like ChatGPT and Bard to enhance their learning. This study compares the use of web-search and AI-based systems to assess how much students' knowledge has improved in an Internet Marketing course. Background While authorship and academic integrity have received a lot of attention in higher education research thus far, using AI effectively provides several benefits for both teaching and learning. In other words, by utilizing these capabilities, AI-based systems may boost students' motivation, interest, and level of knowledge. Methodology The course will teach the students how to use the application of AI (i.e. ChatGPT). To evaluate the efficacy of AI technology, this study will conduct empirical research to compare the knowledge improvement of students who would obtain knowledge about buying sustainable production products in Taiwan between using the web-search systems and the AI-based systems. The assessments of students' knowledge growth are evaluated by using a dependent samples t-test. Contribution This finding expands on prior research examining the possibility of using AI-powered learning aids to inspire students (Al Shamsi et al., 2022; Lund et al., 2023). The ability of ChatGPT to deliver cutting-edge content, personalized feedback, and interactive learning sessions may all contribute to generating curiosity, a sense of accomplishment, and ultimately a greater desire to learn more about subjects. Findings It is anticipated that the AI chatbots created for information delivery might have something to do with students' increased interest in, motivation for, and understanding of their studies. This study examines the potential benefits to students' understanding of sustainable development goals with an AI-specific method of information provision relevant to purchasing items produced sustainably. Recommendations for Practitioners In the near run, using AI systems has been proven to boost university students' sentiments. University students may feel more supported by AI when they use it more regularly. Long-term AI use may cause reliance, especially in the absence of human companionship. Recommendations for Researchers Not all students and instructors have a lot of experiences in teaching and learning with generative AI, despite the fact that it is a relatively new technology. After generative AI is completely incorporated into classrooms, the roles of AI in teaching and learning could reconsider. Impact on Society In order to prepare students for work in a world driven by generative AI, higher education in the future needs change. With an emphasis on in-class and hands-on activities for assessment, new learning outcomes—skills in learning and teaching with AI, AI literacy—and the importance of interdisciplinarity should be highlighted. Future Research Future studies should concentrate on the following topics: interdisciplinary education, creative pedagogies and their evaluation, new assessment and its acceptability.

This article explores the impact of artificial intelligence (AI) on society through the lens of technological determinism and singularity theories. Technological determinism is the notion that technology shapes and controls society and human behaviour. Singularity is a theory that asserts that AI has already become a million times smarter than humans and can self-improve beyond what humans first taught AI applications and machines. The Singularity Theory predicts an intelligence explosion from Artificial General Intelligence soon, in which humans are likely to lose the dominion that they have enjoyed since creation, millions of years ago. AI, in its generative and autonomous or selfimproving state or form, may lead to the automation of many tasks currently performed by humans. This could lead to both benefits and challenges, such as increased efficiency but also job losses. In addition, the article discusses the impact of AI on privacy and raises ethical concerns about the potential misuse when in the hands of bad people. It also discusses ways to ensure that AI is used responsibly and beneficially. This includes governmental authorities developing ethical guidelines for AI development and implementation and ensuring that AI systems are safe, transparent and accountable.

This study presents a comprehensive framework designed to enhance data security in artificial intelligence (AI) systems by integrating robust cybersecurity measures with information governance principles. Employing a convergent parallel mixed-methods design, the research combines a quantitative meta-analysis of 11,245 AI deployments, a qualitative synthesis of 89 governance studies, and technical validations across three open-source AI models (BERT, YOLOv7, and federated learning). Critical threats, including data poisoning (42% success rate), adversarial examples (23% higher vulnerability in vision models), and model inversion (31% more frequent in generative AI), were identified. The proposed framework demonstrated a Composite Security Score (CSS) of 0.87, outperforming existing models (CSS range: 0.78–0.82), and achieved 84–89% attack mitigation and 90–95% regulatory compliance under standards like GDPR and NIST RMF. The framework’s practical contribution lies in offering sector-specific, scalable solutions for strengthening AI system security, thereby enabling healthcare, finance, and public sector organizations to align innovation with ethical and legal obligations. Theoretically, the study advances knowledge by bridging cybersecurity engineering with governance structures, addressing a critical gap in AI risk management. Preliminary evaluations into quantum-ready encryption strategies were explored, though more extensive testing is suggested for future research. Key limitations include reliance on secondary datasets, potential model generalizability constraints, and emerging threats like quantum attacks that require ongoing adaptation. Recommendations advocate sector-specific adoption, quantum-ready encryption, and research into adaptive security and third-party risks to ensure scalable, ethical AI security.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

BackgroundInformation systems based on artificial intelligence (AI) have increasingly spurred controversies among medical professionals as they start to outperform medical experts in tasks that previously required complex human reasoning. Prior research in other contexts has shown that such a technological disruption can result in professional identity threats and provoke negative attitudes and resistance to using technology. However, little is known about how AI systems evoke professional identity threats in medical professionals and under which conditions they actually provoke negative attitudes and resistance.ObjectiveThe aim of this study is to investigate how medical professionals’ resistance to AI can be understood because of professional identity threats and temporal perceptions of AI systems. It examines the following two dimensions of medical professional identity threat: threats to physicians’ expert status (professional recognition) and threats to physicians’ role as an autonomous care provider (professional capabilities). This paper assesses whether these professional identity threats predict resistance to AI systems and change in importance under the conditions of varying professional experience and varying perceived temporal relevance of AI systems.MethodsWe conducted 2 web-based surveys with 164 medical students and 42 experienced physicians across different specialties. The participants were provided with a vignette of a general medical AI system. We measured the experienced identity threats, resistance attitudes, and perceived temporal distance of AI. In a subsample, we collected additional data on the perceived identity enhancement to gain a better understanding of how the participants perceived the upcoming technological change as beyond a mere threat. Qualitative data were coded in a content analysis. Quantitative data were analyzed in regression analyses.ResultsBoth threats to professional recognition and threats to professional capabilities contributed to perceived self-threat and resistance to AI. Self-threat was negatively associated with resistance. Threats to professional capabilities directly affected resistance to AI, whereas the effect of threats to professional recognition was fully mediated through self-threat. Medical students experienced stronger identity threats and resistance to AI than medical professionals. The temporal distance of AI changed the importance of professional identity threats. If AI systems were perceived as relevant only in the distant future, the effect of threats to professional capabilities was weaker, whereas the effect of threats to professional recognition was stronger. The effect of threats remained robust after including perceived identity enhancement. The results show that the distinct dimensions of medical professional identity are affected by the upcoming technological change through AI.ConclusionsOur findings demonstrate that AI systems can be perceived as a threat to medical professional identity. Both threats to professional recognition and threats to professional capabilities contribute to resistance attitudes toward AI and need to be considered in the implementation of AI systems in clinical practice.

The advent of Artificial Intelligence (AI) has revolutionized various domains, and Content Management Systems (CMS) are no exception. Traditional CMS workflows, often reliant on manual processes, have struggled to keep up with the growing demands for scalability, speed, and personalization in managing digital content. AI - powered CMS address these challenges by introducing automation, enhancing efficiency, and delivering personalized user experiences. Through the integration of AI technologies such as Natural Language Processing (NLP), Machine Learning (ML), and Computer Vision, modern CMS have evolved into intelligent platforms capable of automating workflows, optimizing content delivery, and driving data - driven decision - making. This paper explores the transformative potential of AI in CMS, focusing on its applications in workflow automation and user experience enhancement. AI enables the automation of repetitive tasks like content tagging, categorization, and scheduling, significantly reducing manual effort and human error. At the same time, it enhances user engagement by offering personalized content recommendations, optimizing search functionalities, and dynamically adapting content to user contexts. These advancements allow businesses to manage content more effectively and cater to diverse audience needs in real time. However, the integration of AI into CMS is not without challenges. Issues such as data privacy concerns, bias in AI models, and the complexity of integrating AI into legacy systems remain significant barriers. This paper addresses these challenges and proposes actionable recommendations, including the development of explainable AI systems, robust data security frameworks, and scalable architectures that accommodate diverse business needs. As businesses increasingly adopt AI - powered CMS, their potential to transform content management is undeniable. These systems not only streamline workflows and improve operational efficiency but also empower organizations to deliver meaningful and personalized experiences to users. By addressing current challenges and advancing AI technologies, the future of content management will be defined by intelligent, adaptive, and user - centric platforms that bridge the gap between technology and human creativity. This comprehensive review highlights the opportunities and challenges of leveraging AI in CMS and underscores the importance of continued research and innovation to fully realize its potential. AI - powered CMS are not merely tools for managing content but catalysts for digital transformation, enabling organizations to thrive in an increasingly dynamic digital landscape.